Add support for Granite 3.1 model family (IBM) #3261
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
This Pull Request adds support for both the base and instruct versions of the Granite 3.1 model family developed by IBM, which are released as open-source on the Hugging Face platform. These additions expand the options available for various NLP tasks and text generation scenarios.
Main Changes
Added both base and instruct models from the Granite 3.1 family (open-source) to the list of supported models.
Adjusted configuration files to accommodate the specific parameters for these new models.
Benchmarks Executed
The Granite 3.1 models, including both the base and instruct versions, were tested on various benchmarks, including MMLU (Massive Multitask Language Understanding) and HumanEval for code generation. These models demonstrated competitive performance compared to other models of similar scale.